从农场易于收集的母猪数据中估算个体死胎率:贝叶斯网络模型的应用。

IF 3 2区 农林科学 Q1 VETERINARY SCIENCES
Charlotte Teixeira Costa, Gwenaël Boulbria, Christophe Dutertre, Céline Chevance, Théo Nicolazo, Valérie Normand, Justine Jeusselin, Arnaud Lebret
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引用次数: 0

摘要

背景:大量死胎仔猪会对生产和动物福利产生负面影响。死胎率是造成产仔前后仔猪死亡的一个重要原因,并随着多产性的提高而持续上升。本研究的目的是建立一个死胎率预测模型:本研究在位于法国布列塔尼的两个产仔至分娩猪场和一个产仔至断奶猪场进行。每个猪场都记录了总产仔猪数(TB)、活产仔猪数(BA)、死胎仔猪数(S)、前次产仔时的相同数据(TBn- 1、BAn- 1 和 Sn- 1)、产仔前和前次断奶时的背膘厚度以及胎次。使用 BayesiaLab® 软件将贝叶斯网络作为一种综合建模方法来研究与死胎相关的风险因素。我们的研究结果表明,采用混合模型预测死胎率是有效的。该模型确定了三个重要的风险因素:奇数等级(占总互信息的百分比:MI = 64%)、Sn- 1(MI = 25%)和TBn- 1(MI = 11%)。此外,母猪产仔前的背膘厚度也被确定为 5 胎及以上(MI = 0.4%)。实际上,在最佳条件下(即低奇数等级、死胎率低于 8%、上一胎产仔数低于 14 头),我们的模型预测母猪下一胎的死胎率几乎降低了一半,从 6.5%(我们数据集的平均风险)降至 3.5%。相比之下,对于背膘厚度小于 15 毫米、死胎率超过 15%、上一胎产仔数超过 18 头的高龄母猪,风险则增加了 2.5 倍,从 6.5% 增加到 15.7%:我们的研究结果凸显了胎次、前次产仔数和死胎率对死胎概率的影响。此外,必须考虑背膘厚度的重要性,尤其是高龄母猪的背膘厚度。这些信息有助于养殖户根据母猪产死胎的风险对其进行分类和管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the individual stillborn rate from easy-to-collect sow data on farm: an application of the bayesian network model.

Background: A high number of stillborn piglets has a negative impact on production and animal welfare. It is an important contributor to piglet mortality around farrowing and continues to rise with the increase of prolificacy. The objective of this study was to build a predictive model of the stillborn rate.

Results: This study was performed on two farrow-to-finish farms and one farrow-to-wean farm located in Brittany, France. At each farm, the number of total born (TB), born alive (BA), stillborn piglets (S), the same data at the previous farrowing (TBn- 1, BAn- 1 and Sn- 1), backfat thickness just before farrowing and at previous weaning and parity rank were recorded in our dataset of 3686 farrowings. Bayesian networks were used as an integrated modelling approach to investigate risk factors associated with stillbirth using BayesiaLab® software. Our results suggest the validity of a hybrid model to predict the percentage of stillborn piglets. Three significant risk factors were identified by the model: parity rank (percentage of total mutual information: MI = 64%), Sn- 1 (MI = 25%) and TBn- 1 (MI = 11%). Additionally, backfat thickness just before farrowing was also identified for sows of parity five or more (MI = 0.4%). In practice, under optimal conditions (i.e., low parity rank, less than 8% of stillborn piglets, and a prolificacy lower than 14 piglets at the previous farrowing), our model predicted a stillborn rate almost halved, from 6.5% (mean risk of our dataset) to 3.5% for a sow at the next farrowing. In contrast, in older sows with a backfat thickness less than 15 mm, more than 15% of stillborn and a prolificacy greater than 18 piglets at the previous farrowing, the risk is multiplied by 2.5 from 6.5 to 15.7%.

Conclusion: Our results highlight the impact of parity, previous prolificacy and stillborn rate on the probability of stillborn. Moreover, the importance of backfat thickness, especially in old sows, must be considered. This information can help farmers classify and manage sows according to their risk of giving birth to stillborn piglets.

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来源期刊
Porcine Health Management
Porcine Health Management Veterinary-Food Animals
CiteScore
5.40
自引率
5.90%
发文量
49
审稿时长
14 weeks
期刊介绍: Porcine Health Management (PHM) is an open access peer-reviewed journal that aims to publish relevant, novel and revised information regarding all aspects of swine health medicine and production.
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